(449 days)
syngo.CT Brain Hemorrhage is designed to assist the radiologist in prioritizing cases of suspected intracranial hemorrhage on non-contrast CT examinations of the head. It makes case-level output available to a CT scanner or other PACS system for worklist prioritization. The output is intended for informational purposes only and is not intended for diagnostic use. The device does not alter the original medical image and is not intended to be used as a stand-alone diagnostic device.
The subject device syngo.CT Brain Hemorrhage is an image processing software that utilizes artificial intelligence learning algorithms to support qualified clinicians in analysis and prioritization of noncontrast head CT DICOM images by algorithmically identifying findings suspicious of acute intracranial hemorrhage. The subject device provides a pipeline for the analysis and identification of potential ICH as well as a finding notification mechanism.
Here's a breakdown of the acceptance criteria and the study proving the device's performance, based on the provided text:
Acceptance Criteria and Device Performance Study for syngo.CT Brain Hemorrhage
1. Table of Acceptance Criteria and Reported Device Performance
The document states a performance goal rather than strict acceptance criteria with defined thresholds. The acceptance was based on exceeding this goal.
| Performance Metric | Performance Goal (Acceptance Criteria) | Reported Device Performance |
|---|---|---|
| Sensitivity | > 80% | 92.8% (95% CI: 89.3%-95.2%) |
| Specificity | > 80% | 94.5% (95% CI: 91.3%-96.5%) |
| Average Per-Case Processing Time | Comparable to predicate device | 13.67 seconds (95% CI: 7.48-19.86 seconds) |
2. Sample Size and Data Provenance
- Test Set Sample Size: 600 anonymized head CT cases.
- Data Provenance: The cases were collected from 5 sites in the US and Europe. The data was retrospective.
- Case Distribution: Approximately equal distribution of positive (cases with ICH) and negative (cases without ICH) cases.
3. Number and Qualifications of Experts for Ground Truth
- Number of Experts: 3
- Qualifications: US board-certified neuroradiologists with more than 10 years of experience.
4. Adjudication Method for the Test Set
The ground truth was established by majority read of the 3 neuroradiologists. This implies a 2+1 adjudication, where if at least two experts agreed, that decision was taken as the truth.
5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study
No MRMC comparative effectiveness study was mentioned. The study described is a standalone performance study of the algorithm. Therefore, there is no information on how human readers improved with AI vs. without AI assistance. The device is intended solely for worklist prioritization and is not for diagnostic use or to alter images.
6. Standalone (Algorithm Only) Performance Study
Yes, a retrospective standalone performance study was conducted. The reported sensitivity and specificity values are for the algorithm only, without human-in-the-loop.
7. Type of Ground Truth Used
The ground truth was established by expert consensus (majority read of 3 US board-certified neuroradiologists).
8. Sample Size for the Training Set
The document does not specify the sample size used for the training set. It only describes the validation (test) set.
9. How Ground Truth for the Training Set Was Established
The document does not provide information on how the ground truth for the training set was established. It only details how the ground truth for the test set was created (majority read by 3 neuroradiologists).
§ 892.2080 Radiological computer aided triage and notification software.
(a)
Identification. Radiological computer aided triage and notification software is an image processing prescription device intended to aid in prioritization and triage of radiological medical images. The device notifies a designated list of clinicians of the availability of time sensitive radiological medical images for review based on computer aided image analysis of those images performed by the device. The device does not mark, highlight, or direct users' attention to a specific location in the original image. The device does not remove cases from a reading queue. The device operates in parallel with the standard of care, which remains the default option for all cases.(b)
Classification. Class II (special controls). The special controls for this device are:(1) Design verification and validation must include:
(i) A detailed description of the notification and triage algorithms and all underlying image analysis algorithms including, but not limited to, a detailed description of the algorithm inputs and outputs, each major component or block, how the algorithm affects or relates to clinical practice or patient care, and any algorithm limitations.
(ii) A detailed description of pre-specified performance testing protocols and dataset(s) used to assess whether the device will provide effective triage (
e.g., improved time to review of prioritized images for pre-specified clinicians).(iii) Results from performance testing that demonstrate that the device will provide effective triage. The performance assessment must be based on an appropriate measure to estimate the clinical effectiveness. The test dataset must contain sufficient numbers of cases from important cohorts (
e.g., subsets defined by clinically relevant confounders, effect modifiers, associated diseases, and subsets defined by image acquisition characteristics) such that the performance estimates and confidence intervals for these individual subsets can be characterized with the device for the intended use population and imaging equipment.(iv) Stand-alone performance testing protocols and results of the device.
(v) Appropriate software documentation (
e.g., device hazard analysis; software requirements specification document; software design specification document; traceability analysis; description of verification and validation activities including system level test protocol, pass/fail criteria, and results).(2) Labeling must include the following:
(i) A detailed description of the patient population for which the device is indicated for use;
(ii) A detailed description of the intended user and user training that addresses appropriate use protocols for the device;
(iii) Discussion of warnings, precautions, and limitations must include situations in which the device may fail or may not operate at its expected performance level (
e.g., poor image quality for certain subpopulations), as applicable;(iv) A detailed description of compatible imaging hardware, imaging protocols, and requirements for input images;
(v) Device operating instructions; and
(vi) A detailed summary of the performance testing, including: test methods, dataset characteristics, triage effectiveness (
e.g., improved time to review of prioritized images for pre-specified clinicians), diagnostic accuracy of algorithms informing triage decision, and results with associated statistical uncertainty (e.g., confidence intervals), including a summary of subanalyses on case distributions stratified by relevant confounders, such as lesion and organ characteristics, disease stages, and imaging equipment.